Challenge: FinGEAR provides a retrieval framework tailored to financial documents . standard retrieval-augmented generation models underuse financial disclosures .
Approach: FinGEAR combines a finance lexicon for Item-level guidance and hierarchical indices for within-Item search.
Outcome: FinGEAR improves accuracy and accuracy on 10-Ks with a FinQA dataset.

Similar Papers

FinTextQA: A Dataset for Long-form Financial Question Answering (2024.acl-long)

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Challenge: Existing financial question answering datasets lack scope diversity and question complexity.
Approach: They propose to use a dataset for long-form question answering in finance to evaluate QA systems.
Outcome: The proposed dataset includes 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites.
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)

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Challenge: Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge.
Approach: They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage.
Outcome: The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold.
Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents (2025.findings-acl)

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Challenge: standardized documents share similar formats and table structures . this similarity forces traditional RAG methods to misidentify near-duplicate text .
Approach: They propose a hierarchical retrieval framework that performs hierarchically to reduce confusion among similar texts.
Outcome: The proposed framework reduces confusion among similar documents by removing irrelevant passages . it generates complementary queries to collect missing information .
FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering (2026.findings-acl)

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Challenge: Existing reranking frameworks optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents.
Approach: They propose a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema.
Outcome: FINCARDS improves early-rank retrieval over lexical and LLM-based reranking baselines while reducing ranking variance.
RE-FIN: Retrieval-based Enrichment for Financial data (2025.coling-industry)

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Challenge: Financial sentiment analysis (FSA) is a powerful tool to support business decision-making and perform financial forecasting.
Approach: They propose a system that retrieves information from a knowledge base to enrich financial sentences, making them more knowledge-dense and explicit.
Outcome: The proposed system generates propositions from the knowledge base and employs Retrieval-Augmented Generation (RAG) to augment the original text with relevant information.
FinMath: Injecting a Tree-structured Solver for Question Answering over Financial Reports (2022.lrec-1)

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Challenge: Existing models for answering complex questions require multiple-step numerical reasoning.
Approach: They propose a framework that injects a tree-structured neural model into a model to perform multi-step numerical reasoning.
Outcome: The proposed framework improves the previous best model by 8.5% absolute for Exact Match (EM) score and 6.1% absolute for numeracy-focused F1 score.
FinQA: A Dataset of Numerical Reasoning over Financial Data (2021.emnlp-main)

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Challenge: Popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge.
Approach: They propose a large-scale dataset with Question-Answering pairs over financial reports written by financial experts to facilitate analytical progress.
Outcome: The proposed dataset is the first of its kind and is available on github.
Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt (2024.emnlp-industry)

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Challenge: Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings.
Approach: They propose to fine tune financial text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs including both public and proprietary financial documents.
Outcome: The proposed embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embeddING from OpenAI.
Structure Guided Retrieval-Augmented Generation for Factual Queries (2026.acl-long)

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Challenge: Existing methods for RAG produce factually incorrect outputs, resulting in incorrect answers.
Approach: They propose a novel problem that explicitly incorporates structural information into RAG for factual questions to satisfy all query conditions.
Outcome: The proposed method significantly outperforms baselines on ERQA while maintaining reasonable computational overhead.
DocFinQA: A Long-Context Financial Reasoning Dataset (2024.acl-short)

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Challenge: Existing work on automating financial numerical reasoning focuses on unrealistically specific document snippets, failing to reflect the broader and more realistic scenarios faced by analysts.
Approach: They propose a long-document financial QA task that augments 7,437 questions from existing FinQA dataset with full-document context, extending the average context length from under 700 words in FinQA to 123k words in DocFinQA.
Outcome: The proposed task extends the average context length from under 700 words in FinQA to 123k words in DocFinQA.

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